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 Duration 21 hours

Course Outline

Foundations of TinyML in Healthcare

  • Key attributes of TinyML systems
  • Specific constraints and requirements in healthcare contexts
  • Introduction to wearable AI architectures

Biosignal Acquisition and Preprocessing

  • Handling physiological sensor data
  • Methods for noise reduction and signal filtering
  • Extracting features from medical time-series data

Developing TinyML Models for Wearables

  • Choosing appropriate algorithms for physiological data
  • Training models within constrained computational environments
  • Assessing model performance on health-specific datasets

Deploying Models on Wearable Devices

  • Leveraging TensorFlow Lite Micro for on-device inference
  • Integrating AI models into medical wearable hardware
  • Conducting testing and validation on embedded systems

Power and Memory Optimization

  • Strategies to minimize computational overhead
  • Refining data flow and memory utilization
  • Achieving a balance between accuracy and efficiency

Safety, Reliability, and Compliance

  • Regulatory implications for AI-enabled wearables
  • Safeguarding system robustness and clinical usability
  • Implementing fail-safe mechanisms and error management

Case Studies and Healthcare Applications

  • Wearable systems for cardiac monitoring
  • Activity recognition in rehabilitation settings
  • Continuous tracking of glucose levels and biometrics

Future Directions in Medical TinyML

  • Approaches to multi-sensor data fusion
  • Personalized health analytics
  • Next-generation low-power AI chipsets

Summary and Next Steps

Requirements

  • A foundational grasp of core machine learning principles
  • Practical experience with embedded or biomedical devices
  • Proficiency in Python or C-based development

Target Audience

  • Medical and healthcare professionals
  • Biomedical engineers
  • AI developers

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